Foundations of Signal Processing
Cambridge University Press
Edition: 3rd ed., 9/4/2014
EAN 9781107038608, ISBN10: 110703860X
Hardcover, 738 pages, 24.7 x 17.4 x 3.5 cm
Language: English
This comprehensive and engaging textbook introduces the basic principles and techniques of signal processing, from the fundamental ideas of signals and systems theory to real-world applications. Students are introduced to the powerful foundations of modern signal processing, including the basic geometry of Hilbert space, the mathematics of Fourier transforms, and essentials of sampling, interpolation, approximation and compression The authors discuss real-world issues and hurdles to using these tools, and ways of adapting them to overcome problems of finiteness and localization, the limitations of uncertainty, and computational costs. It includes over 160 homework problems and over 220 worked examples, specifically designed to test and expand students' understanding of the fundamentals of signal processing, and is accompanied by extensive online materials designed to aid learning, including Mathematica® resources and interactive demonstrations.
1. On rainbows and spectra
2. From Euclid to Hilbert
2.1 Introduction
2.2 Vector spaces
2.3 Hilbert spaces
2.4 Approximations, projections, and decompositions
2.5 Bases and frames
2.6 Computational aspects
2.A Elements of analysis and topology
2.B Elements of linear algebra
2.C Elements of probability
2.D Basis concepts
Exercises with solutions
Exercises
3. Sequences and discrete-time systems
3.1 Introduction
3.2 Sequences
3.3 Systems
3.4 Discrete-time Fourier Transform
3.5 z-Transform
3.6 Discrete Fourier Transform
3.7 Multirate sequences and systems
3.8 Stochastic processes and systems
3.9 Computational aspects
3.A Elements of analysis
3.B Elements of algebra
Exercises with solutions
Exercises
4. Functions and continuous-time systems
4.1 Introduction
4.2 Functions
4.3 Systems
4.4 Fourier Transform
4.5 Fourier series
4.6 Stochastic processes and systems
Exercises with solutions
Exercises
5. Sampling and interpolation
5.1 Introduction
5.2 Finite-dimensional vectors
5.3 Sequences
5.4 Functions
5.5 Periodic functions
5.6 Computational aspects
Exercises with solutions
Exercises
6. Approximation and compression
6.1 Introduction
6.2 Approximation of functions on finite intervals by polynomials
6.3 Approximation of functions by splines
6.4 Approximation of functions and sequences by series truncation
6.5 Compression
6.6 Computational aspects
Exercises with solutions
Exercises
7. Localization and uncertainty
7.1 Introduction
7.2 Localization for functions
7.3 Localization for sequences
7.4 Tiling the time–frequency plane
7.5 Examples of local Fourier and wavelet bases
7.6 Recap and a glimpse forward
Exercises with solutions
Exercises.